Early detection of certain diseases, such as Alzheimer's dementia (AD), is critical for treatment success and a high priority research area. The development of disease-modifying treatment strategies requires objective characterization techniques and in vivo quantitative biomarkers that are able to identify the disease with higher accuracy and at a much earlier stage than clinically based assessment (Vellas, B., et al., Disease-modifying trials in Alzheimer's disease: a European task force consensus. Lancet Neurol, 2007. 6(1): p. 56-62).
Medical images, and in particular standard magnetic resonance imaging (MRI) sequences (T1, T2 or PD-weighted) on 1 to 3 Tesla clinical scanners, can show pathologically related changes in cortical and sub-cortical structures (Csernansky, J. G., et al., Correlations between antemortem hippocampal volume and postmortem neuropathology in AD subjects. Alzheimer Dis Assoc Disord, 2004. 18(4): p. 190-5; Kloppel, S., et al., Automatic classification of MR scans in Alzheimer's disease. Brain, 2008). Global, regional and local cerebral morphology alterations, such as tissue atrophy, are reflections of the microscopic disease progression. Analysis of structural MRI allows the in vivo assessment of these changes, and therefore can be used as a quantitative biomarker in AD (Weiner, M., et al., The Use of MRI and PET or Clinical Diagnosis of Dementia and Investigation of Cognitive Impairment: A Consensus Report. 2005, Alzheimer's Association; Chetelat, G. and J. C. Baron, Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging. Neuroimage, 2003. 18(2): p. 525-41; Davatzikos, C., et al., Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging, 2008. 29(4): p. 514-23).
In previous work (Duchesne, S., et al., MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls. IEEE Trans Med Imaging, 2008. 27(4): p. 509-20. [7]), applicants developed a novel, high-dimensional classification approach based on data reduction techniques of MRI image attributes, defined as the combination of intensity and shape characteristics. The technique was tested in a series of pilot studies that used single-time point T1w MRI for the differentiation of normal aging from AD.
Due to the important human and financial costs of certain diseases (e.g. Alzheimer's), an automated quantitative biomarker enabling effective and early disease identification, based on medical image data, would permit earlier treatment initiation and be useful to reduce patient suffering and costs to primary caregivers and health care systems.